Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation

• URL: http://arxiv.org/abs/2409.11535v1
• Date: Tue, 17 Sep 2024 20:13:32 GMT
• Title: Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation
• Authors: Michael Lingzhi Li, Shixiang Zhu,
• Abstract summary: We introduce a novel framework called generative curation, which optimize the true desirability of decision options by integrating both quantitative and qualitative aspects. We propose two implementation approaches: a generative neural network architecture that produces a distribution $pi$ to efficiently sample a diverse set of near-optimal actions, and a sequential optimization method to iteratively generate solutions. We validate our approach with extensive datasets, demonstrating its effectiveness in enhancing decision-making processes across a range of complex environments.
• Score: 6.980546503227467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The surge in data availability has inundated decision-makers with an overwhelming array of choices. While existing approaches focus on optimizing decisions based on quantifiable metrics, practical decision-making often requires balancing measurable quantitative criteria with unmeasurable qualitative factors embedded in the broader context. In such cases, algorithms can generate high-quality recommendations, but the final decision rests with the human, who must weigh both dimensions. We define the process of selecting the optimal set of algorithmic recommendations in this context as human-centered decision making. To address this challenge, we introduce a novel framework called generative curation, which optimizes the true desirability of decision options by integrating both quantitative and qualitative aspects. Our framework uses a Gaussian process to model unknown qualitative factors and derives a diversity metric that balances quantitative optimality with qualitative diversity. This trade-off enables the generation of a manageable subset of diverse, near-optimal actions that are robust to unknown qualitative preferences. To operationalize this framework, we propose two implementation approaches: a generative neural network architecture that produces a distribution $\pi$ to efficiently sample a diverse set of near-optimal actions, and a sequential optimization method to iteratively generates solutions that can be easily incorporated into complex optimization formulations. We validate our approach with extensive datasets, demonstrating its effectiveness in enhancing decision-making processes across a range of complex environments, with significant implications for policy and management.

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